About the Role:We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.
Key Responsibilities:- Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis
- Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments
- Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection
- Develop, validate and deploy computational workflows to optimize the "Design-Make-Test-Analyze" cycles and address gaps
Required Qualifications:- Ph.D. in Computational Chemistry, Biophysics, or a related field
- 1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery
- Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact
- Exceptional ability to communicate the "why" behind a design to a diverse scientific audience
- Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc
- A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones
Preferred Qualifications:- Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques
- Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization
- A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones
Skills & Competencies:- Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)
- Ability to work directly in a Linux-based environment
- Familiarity with cloud computing infrastructure (AWS, GCS) is a plus
- Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)
Benefits:Superluminal offers a comprehensive benefits package that fully covers employees' annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.
